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Johnson & Johnson

Makes prescription drugs and medical devices

Director, AI Security

Security Engineering & ArchitecturePosted on Nov 10
Full-time

About the Job

Requirements

  • A bachelor's degree in Information Security, Computer Science, Engineering, Information Systems, or a related discipline, or equivalent experience.
  • At least 12 years of progressive experience in cybersecurity, application security, security engineering, or related disciplines, including leadership of enterprise security capabilities, plus recent hands-on experience securing AI-enabled systems.
  • Demonstrated experience implementing, operating, and scaling AI security capabilities across end-user generative AI services, AI applications, agents, and models, with measurable improvements in security coverage and risk reduction.
  • A working understanding of AI and machine learning lifecycles, large language models, retrieval-augmented generation, model APIs, and agent tool integrations, including security implications across development, deployment, and inference.
  • Practical expertise in AI discovery, identity and access enforcement, least-privilege agent permissions, secrets protection, data loss prevention, and runtime safeguards, with demonstrated ability to assess and mitigate prompt injection, sensitive data exposure, excessive agency, tool misuse, insecure output handling, model theft, adversarial manipulation, data and model poisoning, and AI supply chain compromise.
  • Experience in cloud and platform security, including securing the cloud, data, and identity platforms on which AI workloads and services run.
  • Experience integrating AI security telemetry with security operations and incident response processes in partnership with Cyber Defense, including detection engineering, investigation, containment, and remediation of threats affecting AI systems and models.
  • Proven ability to lead technical security teams, manage vendors, budgets, and concurrent capability deployments, and partner with engineering, security operations, and business stakeholders across a global, highly matrixed enterprise.
  • Strong executive stakeholder management, technical judgment, and communication skills, with the ability to translate emerging AI threats into prioritized defenses and communicate business impact, control effectiveness, security coverage, and remediation outcomes.

Responsibilities

  • Establish and scale enterprise AI security capabilities, including LLM model and harness security and lifecycle management, agent security and non-human identity, end-user AI usage management, AI supply chain security, AI runtime protection, lifecycle assurance, ongoing observability and monitoring, and AI application security.
  • Establish AI discovery capabilities to identify enterprise use of generative AI, AI applications, agents, and models across the full AI footprint, and prioritize gaps in security coverage.
  • Establish end-user AI usage management, including access enforcement and data protection controls for generative AI use, to prevent unauthorized access and sensitive information exposure.
  • Own AI agent identity and authorization security, including non-human identities, credentials, service accounts, and least-privilege permissions, working with the IAM team on agentic identity.
  • Partner with stakeholder teams to integrate security testing and protective controls throughout AI system development, deployment, and ongoing operation.
  • Establish AI onboarding and assurance, including security evaluation of new AI capabilities before production: security assessment and ongoing management of new models, agent security reviews, AI red-team guidance, supply chain assurance, and runtime monitoring requirements.
  • Define security approval criteria for production deployment of AI models, agents, and applications, including emerging and frontier model capabilities.
  • Implement model and AI supply chain security controls to detect and mitigate model and data poisoning, compromised components, and exposures affecting deployed models.
  • Ensure continuous adversarial testing of models, AI applications, and agent workflows to identify prompt injection and other high-impact threats, validate defenses, and verify remediation.
  • Integrate AI security telemetry and detections into security operations, and partner with Cyber Defense on AI-specific detection, triage, and incident response playbooks.
  • Deploy frontier AI and other advanced models to identify and prioritize cybersecurity exposures, accelerate investigations with Cyber Defense and GSO, and expedite remediation with appropriate safeguards and human validation.
  • Partner with AI Governance, Privacy, Security and Technology Architecture, and Risk Management teams to enable security controls across AI technologies through enterprise AI standards, onboarding processes, and operational guardrails.
  • Own AI security vendor relationships and budget, including vendor selection, performance management, spend, vendor strategy, and capability rationalization.
  • Partner with Vulnerability Management, Application Security, and business teams to drive timely remediation of AI-related exposures.
  • Be accountable for enterprise AI security standards adoption, measurement of AI security control effectiveness, executive risk communication, and the AI security roadmap and investment planning in partnership with Security Architecture.
  • Balance delivery speed, regulatory and enterprise obligations, technical risk, stakeholder expectations, operational continuity, and value realization as an enterprise-minded cybersecurity leader.
  • Maintain security by design through clear technical direction, informed challenge, disciplined governance, and partnership with enterprise policy and capability owners.
  • Partner with AI Governance, Privacy, Technology Architecture, and Risk Management teams to keep role boundaries clear and embed security into enterprise AI standards and processes.
  • Build repeatable, scalable systems that emphasize measurable outcomes, traceability, and continuous improvement.
  • Drive accountability by defining ownership, escalation paths, decision rights, quality standards, and delivery expectations.
  • Create an inclusive, collaborative environment that encourages mentoring, knowledge sharing, pragmatic risk management, and high-quality execution.

Desired Qualifications

  • An advanced degree, MBA, MS, or equivalent.
  • Experience securing production multi-agent systems and Model Context Protocol integrations, including delegated authorization, isolated tool execution, inter-agent trust boundaries, and defenses against memory poisoning and cascading compromise.
  • Deep technical experience evaluating open-weight and fine-tuned models for backdoors, poisoning, model extraction, and training-data leakage, and building automated adversarial evaluation suites informed by NIST adversarial machine learning guidance, MITRE ATLAS, and OWASP AI security guidance.
  • Experience embedding AI security testing and controls into software delivery and MLOps pipelines, including model and dependency assessment, adversarial testing, AI red teaming, remediation validation, and repeatable evaluation of models, applications, and agent workflows.
  • Advanced machine learning engineering or frontier model research experience, including model development, fine-tuning, and evaluation, and adversarial AI research or specialized red-team experience.
  • Working knowledge of the NIST AI Risk Management Framework and Generative AI Profile, OWASP guidance for LLM and agentic application security, and MITRE ATLAS, with the ability to translate threats and recommended practices into operational controls and security tests.
  • Practical experience applying frontier AI or other advanced models to cybersecurity use cases, including exposure identification, risk prioritization, investigation, and remediation automation, with a track record of moving frontier-model-powered vulnerability discovery and remediation from pilot to sustained production use, including validation of generated fixes, bounded execution, approval checkpoints, rollback, and quantified improvements in remediation speed and quality.
  • Experience scaling AI security across cloud-hosted and self-hosted models in regulated healthcare, pharmaceutical, or MedTech environments, including protection of research, clinical, or manufacturing data and validation of controls without disrupting critical workflows.
  • Demonstrated contributions to the AI security field through responsibly disclosed vulnerabilities, published applied research, open-source security or evaluation tools, or substantive contributions to OWASP, MITRE ATLAS, or comparable industry initiatives, with evidence of translating findings into deployed defenses.
  • Experience with skill security and multi-turn agentic (loop prompt) security.
  • Relevant certifications such as CISSP, CISM, or equivalent credentials; AI security certifications such as GIAC AI Platform Security, GIAC AI Security Automation Engineer, or ISACA Advanced in AI Security Management are particularly desirable.

About the Company

Johnson & Johnson

Makes prescription drugs and medical devices

Johnson & Johnson develops prescription medicines and makes medical devices for hospitals, and unlike most big drugmakers, it runs both businesses under one roof. In 2015, its Darzalex became the first antibody drug approved for multiple myeloma, a blood cancer. Its medicines treat cancer, immune diseases such as psoriasis, and mental illnesses such as depression, and patented drugs bring in most of its sales. Its MedTech unit makes heart pumps, catheters that treat irregular heartbeats, surgical sutures, and Acuvue contact lenses. It was founded in 1886 to make sterile surgical dressings, is based in New Brunswick, New Jersey, and spun off its consumer brands, such as Tylenol and Band-Aid, as Kenvue in 2023.

Company size
10,001+
Company stage
IPO
Headquarters
New Brunswick, NJ
Founded
1886

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